Despite advances in paperless systems and technology, many companies still receive physical, handwritten documents containing highly-sensitive information from customers. Over the years, handwriting recognition software (typically using optical character recognition (OCR) techniques), has had some commercial success in determining the content of handwritten forms and other documents when converted into a digital version, such as an image. However, the accuracy of currently available commercial solutions has been a barrier to scale automation of physical document processing. For example, current solutions based on OCR offer handwriting recognition accuracy of approximately 90%, which is unacceptable when important information (e.g., social security numbers, account numbers, etc.) must be recognized without any errors. Due to its limitations, OCR is seen merely as a complement to trained human document reviewers and doesn't yield end-to-end automation.
With the recent popularity of machine learning and neural network algorithms, accuracy has improved—currently-available handwriting recognition software based on machine learning and neural networks is generally 95% accurate. However, this level of accuracy is still insufficient as errors continue to occur.